How to Use Automation to Optimize Big Data Projects

The advent of big data has been disrupting organizations and bringing big changes in the digital landscape. By 2026, big data applications and analytics are projected to grow to $19.4B. For every organization, the main element of big data lies in finding out the patterns which can actually provide insights on business efficiency and customer behavior.

However, the problem is that some organizations are still finding it difficult to consolidate and interpret large volumes of data. Nearly, 55% B2B companies believe that the inability to merge data from a range of sources in a timely manner is holding them back from achieving business goals.

To successfully leverage data, along with investing in the right big data tools and applications, it is crucial to connect your big data applications with your enterprise and legacy systems. So how can an organization achieve this? The answer is automation. Enterprises can tackle Big Data with ‘Big Workflows’; built in an automation tool that helps them maximize available resources and streamline the data handling process.

Manage big data using automation

Automation helps organizations unify tools and applications in a way that the right data is always shared with the right people on time. It provides pre-built, tested logic that allows teams to focus on growth objectives, rather than writing code and testing workflows.

A Look at Big Data Workflow Automation

In the same way that Big Data involves large and complex sets of data, Big Data workflows are bigger, complex processes that help enterprises make sense of the data. The key feature that differentiates automation from manual data processing is its ability to transform data at lightning-quick speeds with zero errors. Many of the pitfalls commonly associated with big data projects can be minimized or removed completely with workflow automation.

For starters, an automation tool can help you with the following tasks:

Deliver data anywhere, anytime in the desired format

Load and validate data on demand

Replicate data and share it across platforms

Display data in real-time

Offer analytics in addition to the data

Identify data features to build a predictive analysis model

Send a notification when a system is updated

Query enormous networks

Generate giant maps

Schedule a workflow to run when required

and more!

Benefits of introducing automation in big data processes

1. Eliminate project bottlenecks

The more steps a data management process has, the greater is the likelihood of barriers emerging along the way. Additionally, the lack of a streamlined process can result in data duplication. Automation can help you tackle this by capturing and storing big data as it emerges — in any volume, velocity or variety. You can then share this data with different teams and across all your applications in real-time.

2. Reduce dependence on physical resources

If your employees are still viewing data by printing it out and reading the paperwork, it can cause a huge strain on their productivity. You can alleviate this situation by investing in an automation solution. It collates information from disparate sources, transforms it into a comprehensible format and distributes the reports to the appropriate teams online. Instead of storing physical documents in bulky filing cabinets, teams can simply pull up reports on their screens.

3. Transform data into insights

Data can be considered valuable only when it produces meaningful insights. With automation, organizations can easily incorporate big data into their business workflows and gain deep insights almost instantaneously. This means less time spent on preparing and integrating data, and more time for analyzing it. Automation creates a master dataset by combing all data into a single layer and offers businesses quick access to comprehensive dashboards and business intelligence reports.

4. Reduce complexity in all environments

Big data workflows usually consist of various steps and require multiple technologies deployed in different environments. The solution to this is creating a fused dataset using automation. Most automation solutions provide an agnostic platform, which supports on-premise, hybrid, and multi-cloud environments. It efficiently manages big data processes across different environments and provides the teams with a holistic view of all business data.

5. Improve the reliability of processes

Real-time monitoring of big data workflows can help minimize errors and service interruptions. An automation solution allows you to manage your entire big data workflow from the same screen. If something goes wrong, you immediately get notified about what happened and where it happened.

6. Better decision making

With the use of big data workflow automation, organizations can accelerate the decision-making process. It arms them with the right kind of data at the right time, preparing them to always stay one step ahead of industry peers. An automation solution can collect and structure data in a matter of minutes and consequently make it readily available for easy access. This allows teams to identify possible risks and weak areas, understand customer behavior, and optimize business strategies.

Removing big data complexity with Built.io

Built.io, a cloud-first workflow automation platform helps businesses fast-track the integration of big data projects. It connects disparate enterprise applications, databases, and platforms to support the orchestration of data from any information source.

Built.io provides integrations with more than 180 services and numerous ready-to-use templates, allowing you to scale your operations to accommodate hundreds of data flows. To learn more about how it can help you solve your big data challenges, sign up with us today.